Electric Load Forecasting Using BP Neural Networks

Application of BP Neural Networks in Electric Load Forecasting

Electric load forecasting is a critical component in power system planning and operation. Accurate predictions help power companies optimize resource allocation and enhance grid stability. As a classic artificial neural network model, BP neural networks are frequently employed for solving complex time-series prediction problems due to their strong nonlinear fitting capabilities.

Data Preprocessing Before forecasting, historical electric load data requires cleaning and normalization to eliminate dimensional differences, along with partitioning into training and test sets. A sliding window approach is commonly used to construct samples, converting continuous load data into input-output pairs required for supervised learning. In MATLAB implementation, this involves using functions like `normalize` for data scaling and creating time-lagged features using array indexing operations.

Network Architecture Design In MATLAB, the network is constructed by setting the number of input layer nodes (corresponding to historical data time steps), the number of hidden layers and their neurons (typically determined through trial and error), and output layer nodes (representing predicted future load values). The hidden layer commonly uses Sigmoid or ReLU activation functions, while the output layer typically employs a linear function. Code implementation involves using the `feedforwardnet` function with specified hidden layer sizes and activation functions.

Training and Parameter Tuning The backpropagation algorithm combined with gradient descent optimizes weights, with key parameters like learning rate and iteration count requiring careful adjustment to prevent overfitting or underfitting. MATLAB's Neural Network Toolbox (using functions like `trainlm` for Levenberg-Marquardt optimization) simplifies the training process. The training function automatically calculates gradients and updates network parameters through iterative optimization.

Result Validation Simulation diagrams typically compare predicted curves against actual load curves, with accuracy quantified through Mean Squared Error (MSE) or Mean Absolute Percentage Error (MAPE). If lagging or amplitude deviations occur, potential solutions include increasing network depth or implementing feature engineering to optimize input data. The `perform` function in MATLAB calculates these error metrics automatically after prediction.

Extension Considerations: Combining LSTM networks can better capture long-term temporal dependencies Integrating external variables like weather conditions and holiday schedules enhances prediction robustness